In recent years, developers have generated a variety of hardware and software platforms for creating digital drawings. Indeed, utilizing these platforms, computing systems can now create, edit, save, and share vector drawings, such as drawings that include Bézier curves. Unlike raster images (e.g., images made up of pixels), vector drawings are scalable to any size without creating blurring or artifacts and, in many cases, are stored more compactly than raster images. However, despite the advantages of vector drawings, many electronic devices such as digital cameras and scanners still produce raster images. For example, most digital images found online today are created and stored as raster images.
Because of the advantages of vector drawings over raster images, individuals often desire to convert raster images into vector drawings. To convert a raster image, conventional image conversion systems generally offer two options. First, conventional systems track a plurality of user interactions of manually tracing the raster image with vector curves to convert the raster image to a vector drawing. Alternatively, conventional systems employ a batch conversion process that converts the entire raster image to a vector drawing. As discussed below, each of these options includes numerous disadvantages and shortcomings.
Regarding digital tracing of a raster image, conventional systems are inaccurate and inefficient. For example, conventional systems require users to manually trace the edge of a raster image by meticulously maneuvering a cursor precisely over the edge desired to be traced. Accordingly, the process of a user manually tracing the edge in a raster image is a tedious and time-consuming process and often results in a user tracing and re-tracing an edge until an accurate line tracing results.
In addition to being extremely burdensome to a user, conventional systems using digital tracing require considerable processing power and time to convert the raster image to a vector drawing. For instance, manually tracing pixels with a digital tool requires tracking numerous user interactions over a long period of time (in addition to significant training and experience by the user) to achieve adequate results. Indeed, because conventional systems require tracing of pixels, considerable time is needed to convert the raster image to a vector drawing. Further, even when using the assistance of digital tools, such as a snapping pen tool, tracing entire edges often produces unsatisfactory results. Indeed, conventional systems often generate vector drawings of non-optimal fit because they consider small incremental pieces of the edge rather than the whole edge at once. For instance, these assistive tools often miss endpoints, corners, and sharp turns of edges in the raster image, which result in poorly fitted curves. Overall, digital tracing produces poor results and causes inefficiencies to both computing resources and associated users.
The batch conversion process also produces inaccurate results and computing inefficiencies. For instance, the batch conversion processes a raster image by rigidly converting all edges found in the image into parametric curves (e.g., Bézier curves). After batch processing, conventional systems require significant user input, wasting significant time to delete undesirable and additional curves while fixing inaccuracies caused by the batch conversion process (e.g., incorrectly connected edges or inaccurate parametric curves). This “weeding out” process increases time for generating raster images as users find and fix unnecessary or erroneous parametric curves.
Moreover, converting all edges in the raster image causes computing devices to waste unnecessary processing power and memory resources. For instance, conventional image conversion systems waste needless processing resources converting edges from the raster image into vectors often for an individual to then remove many of the vectors and correct other vectors. Furthermore, during the conversion process, conventional systems often require a computing device to store generated conversion data, which requires additional storage space. Indeed, computing devices generate and store conversion data for many of the vectors that the individual then removes or changes, which wastes computer memory and storage resources.
As a further drawback, conventional image conversion systems employ complex processes to convert a raster image into a vector drawing. Often, because of the complexity, an individual must wait for the conversion to occur. Furthermore, due to the complexities and storage constraints mentioned above, many portable computing devices are incapable of converting an entire raster image (high-resolution raster images, in particular) to a vector drawing.
These along with additional problems and issues exist with conventional image conversion systems.